Job
- Level
- Senior
- Job Field
- Data
- Employment Type
- Full Time
- Contract Type
- Temporary employment
- Location
- Berlin
- Working Model
- Full Remote
Job Summary
In this role, you will develop machine learning and AI solutions to detect and analyze humanitarian crises by fusing data from multiple sources and building automated monitoring and reporting systems.
Job Technologies
Your role in the team
- We're looking for an experienced Data Scientist to help shape the future of humanitarian action through CLEAR (Crisis Learning, Early Warning and Anticipatory Action) - NRC's open infrastructure designed to transform fragmented crisis data into timely, actionable intelligence.
- As the Data Scientist, you will play a pivotal role in designing and deploying cutting-edge machine learning and deep learning solutions that support early warning systems and humanitarian decision-making.
- You will develop predictive models capable of identifying emerging crisis patterns, forecasting humanitarian needs, and transforming complex, multimodal datasets into practical insights for field operations.
- Working closely with the AI Lead, software developers, and data engineers, you will contribute across the full machine learning lifecycle - from model development and optimisation to performance monitoring and deployment.
- Support the design and implementation of CLEAR's data architecture.
- Partner with developers and data engineers to design and stand up the environment needed to train and fine-tune models (including data ingestion pipelines, compute and GPU resources, experiment tracking and MLOps tooling) actively shaping that environment rather than waiting for it to be provided.
- Develop machine learning and deep learning models that integrate multiple data streams to detect early indicators of humanitarian crises, combining earth observation and satellite imagery, conflict event data, climate monitoring, economic indicators, and population movement patterns into unified risk assessment frameworks.
- Build computer-vision and remote-sensing models on satellite and aerial imagery (for example flood-extent mapping, building and settlement detection, displacement-site monitoring, infrastructure damage assessment and land-cover change detection) and fuse these earth observation outputs with non-imagery signals.
- Contribute to building automated alert systems that identify emerging crises, calibrating prediction algorithms for different crisis types.
- Create ensemble modelling approaches that combine traditional statistical methods with advanced AI techniques.
- Fine-tune and adapt foundation models and large language models to humanitarian use cases such as document triage, multilingual report analysis, and situation summarisation.
- Explore venues for adapting models to evolving crisis conditions through reinforcement learning systems.
- Implement impact-based forecasting systems that translate meteorological, conflict, and economic predictions into specific humanitarian consequences such as displacement volumes, food insecurity levels, and infrastructure damage estimates.
- Build decision trees and recommendation engines that guide field staff through systematic needs assessment processes informed by predictive analytics and historical response data.
- Create automated reporting systems and interactive dashboards that enable field teams and leadership to access real-time data for rapid response activities.
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Our expectations of you
Education
- Advanced degree in Data Science, Statistics, Computer Science, Physics, Engineering, Economics or a related quantitative field, with a minimum of 5 years of professional experience in applied data science.
Qualifications
- Strong SQL skills for database management and complex query optimization.
- A proven track record of proactively sourcing and engineering data (finding, negotiating access to, cleaning and, where necessary, generating data).
- Ability to create automated reporting systems and dashboards using tools like Tableau, Power BI or similar platforms.
- Understanding of model deployment and MLOps practices, and comfort working with engineers to provision the infrastructure models need.
- Fundamental skills with version control software and collaborative development.
- Fluency in written and spoken English. Other languages are an asset.
- Understanding of ensemble methods and explainable AI techniques for transparent decision-making.
- Applying computer vision and deep learning to imagery (semantic segmentation, object detection, change detection) for humanitarian tasks such as flood-extent mapping, damage assessment, settlement and displacement-site detection, infrastructure monitoring, and population estimation (a strong asset).
Experience
- Demonstrated experience designing, training and fine-tuning deep learning models (e.g. CNNs, recurrent/sequence models and transformers), including how to structure training runs, manage compute, and diagnose and improve model performance.
- Advanced proficiency in Python for statistical analysis, machine learning and data manipulation, with experience in key libraries including pandas, NumPy, scikit-learn, TensorFlow, PyTorch and Keras.
- Hands-on experience implementing supervised and unsupervised learning algorithms including regression models, classification techniques, clustering methods, and time series analysis.
- Experience designing and implementing ETL pipelines for processing datasets from multiple sources, with skills in data cleaning, transformation and quality assurance at scale.
- Experience working with large, messy, real-world datasets.
- Practical experience working in low-data or data-scarce settings, including transfer learning, few-shot learning, data augmentation, and synthetic data generation to overcome limited training data.
- Experience implementing and fine-tuning large language models (LLMs) for applied tasks.
- Experience with natural language processing techniques for analyzing reports, social media or news data relevant to crisis monitoring.
- Experience using GenAI for automated analysis of large volumes of documents - extracting key themes, sentiment analysis and identifying emerging trends across multiple contexts and languages.
- Experience working with earth observation and satellite imagery; optical (e.g., Sentinel-2, Landsat) and radar / SAR (e.g., Sentinel-1), alongside commercial high-resolution sources, and with geospatial tooling such as Google Earth Engine, rasterio / GDAL, xarray, and geopandas.
- Experience with multimodal data fusion.
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What we offer
- Duty station: Remote (Germany, France, UK or Belgium)
- Contract: Fixed term (2 years)
- Travel: Up to 10%
- Salary/benefits: Grade 9 on NRC's Resident salary scale, with accompanying terms and conditions.
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Topics that you deal with on the job
Job Locations
This is your employer
Norwegian Refugee Council
The Norwegian Refugee Council (NRC) is a humanitarian organization based in Oslo that is dedicated to the protection and support of refugees and internally displaced persons worldwide. With around 15,000 employees, the organization operates in over 40 countries, providing both immediate and long-term assistance to millions of people.
Description
- Company Type
- Established Company
- Working Model
- Full Remote
- Industry
- NGO, NPO, Associations
Data Scientist
- Location
- Berlin
- Working Model
- Full Remote
- Diversity
- Open for all genders
- English Only
- English only required
